Capacity Forecasting Service Optimizes Virtual Machine Workload Allocation
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Solution Overview
Problem
Service provider networks face challenges in optimizing the allocation of computing resources among multiple workloads with varying demands, leading to underutilization of reserved compute instances and increased costs due to the lack of efficient sharing and scheduling of resources.
Innovation Solution
A capacity forecasting and scheduling service that monitors historical usage patterns, predicts future resource needs, and intelligently allocates excess resources to other workloads, while providing graphical user interfaces for users to manage and visualize resource utilization, ensuring efficient use and reducing waste.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If computing resources are reserved for multiple workloads, then resource availability is improved, but resource utilization efficiency deteriorates due to underutilization
Solution Approach 1:
The system dynamically adjusts resource allocation between workloads based on predicted usage patterns. The capacity forecasting service continuously monitors and adapts resource distribution, transforming static reserved capacity into dynamic, optimized allocations that maintain availability while improving utilization efficiency
Solution Approach 2:
The system performs preliminary forecasting of capacity usage before allocation decisions are made. By predicting future resource needs in advance, the system can proactively optimize resource distribution across workloads, preventing both underutilization and resource shortages before they occur
2Adaptability or versatility
If computing resources are allocated to multiple workloads, then workload support capability is improved, but cost increases due to lack of efficient sharing
Solution Approach 1:
The system enables computing resources to serve multiple workloads simultaneously through the capacity sharing mechanism. Reserved capacity can be dynamically allocated to different workloads based on predicted needs, allowing the same physical resources to fulfill multiple functions and support diverse workloads without proportionally increasing costs
Solution Approach 2:
The system changes the allocation parameters of computing resources based on forecasted usage patterns. By adjusting resource distribution parameters dynamically rather than maintaining fixed allocations, the system reduces waste and lowers costs while maintaining the ability to support multiple workloads
3Measurement precision
If forecasting accuracy is improved, then resource allocation precision is improved, but system complexity increases
Solution Approach 1:
The capacity forecasting and scheduling service acts as an intermediary layer between resource monitoring and resource allocation. This intermediary component handles the complexity of forecasting algorithms and optimization logic, providing accurate predictions and allocation decisions without requiring complex modifications to the underlying resource management infrastructure
Data Source
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AI summary
Techniques are described for optimizing the allocation of computing resources provided by a service provider network-for example, compute resources such as virtual machine (VM) instances, containers, standalone servers, and possibly other types of computing resources-among computing workloads associated with a user or group of users of the service provider network. A service provider network provides various tools and interfaces to help businesses and other organizations optimize the utilization of computing resource pools obtained by the organizations from the service provider network, including the ability to efficiently schedule use of the resources among workloads having varying resource demands, usage patterns, relative priorities, execution deadlines, or combinations thereof. A service provider network further provides various graphical user interfaces (GUIs) to help users visualize and manage the historical and scheduled uses of computing resources by users' workloads according to user preferences